/*=========================================================================

  Program:   ORFEO Toolbox
  Language:  C++
  Date:      $Date$
  Version:   $Revision$


  Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
  See OTBCopyright.txt for details.


     This software is distributed WITHOUT ANY WARRANTY; without even
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/

#include "otbImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkConstNeighborhoodIterator.h"
#include "itkImageRegionIterator.h"

// Software Guide : BeginLatex
//
// In this example, the Sobel edge-detection routine is rewritten using
// convolution filtering.  Convolution filtering is a standard image processing
// technique that can be implemented numerically as the inner product of all
// image neighborhoods with a convolution kernel \cite{Gonzalez1993}
// \cite{Castleman1996}.  In ITK, we use a class of objects called
// \emph{neighborhood operators} as convolution kernels and a special function
// object called \doxygen{itk}{NeighborhoodInnerProduct} to calculate inner
// products.
//
// The basic ITK convolution filtering routine is to step through the image
// with a neighborhood iterator and use NeighborhoodInnerProduct to
// find the inner product of each neighborhood with the desired kernel. The
// resulting values are written to an output image.  This example uses a
// neighborhood operator called the \doxygen{itk}{SobelOperator}, but all
// neighborhood operators can be convolved with images using this basic
// routine.  Other examples of neighborhood operators include derivative
// kernels, Gaussian kernels, and morphological
// operators. \doxygen{itk}{NeighborhoodOperatorImageFilter} is a generalization of
// the code in this section to ND images and arbitrary convolution kernels.
//
// We start writing this example by including the header files for the Sobel
// kernel and the inner product function.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkSobelOperator.h"
#include "itkNeighborhoodInnerProduct.h"
// Software Guide : EndCodeSnippet

int main(int argc, char * argv[])
{
  if (argc < 4)
    {
    std::cerr << "Missing parameters. " << std::endl;
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0]
              << " inputImageFile outputImageFile direction"
              << std::endl;
    return -1;
    }

  typedef float                           PixelType;
  typedef otb::Image<PixelType, 2>        ImageType;
  typedef otb::ImageFileReader<ImageType> ReaderType;

  typedef itk::ConstNeighborhoodIterator<ImageType> NeighborhoodIteratorType;
  typedef itk::ImageRegionIterator<ImageType>       IteratorType;

  ReaderType::Pointer reader = ReaderType::New();
  reader->SetFileName(argv[1]);
  try
    {
    reader->Update();
    }
  catch (itk::ExceptionObject& err)
    {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return -1;
    }

  ImageType::Pointer output = ImageType::New();
  output->SetRegions(reader->GetOutput()->GetRequestedRegion());
  output->Allocate();

  IteratorType out(output, reader->GetOutput()->GetRequestedRegion());

// Software Guide : BeginLatex
//
// \index{convolution!kernels}
// \index{convolution!operators}
// \index{iterators!neighborhood!and convolution}
//
// Refer to the previous example for a description of reading the input image and
// setting up the output image and iterator.
//
// The following code creates a Sobel operator.  The Sobel operator requires
// a direction for its partial derivatives.  This direction is read from the command line.
// Changing the direction of the derivatives changes the bias of the edge
// detection, i.e. maximally vertical or maximally horizontal.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
  itk::SobelOperator<PixelType, 2> sobelOperator;
  sobelOperator.SetDirection(::atoi(argv[3]));
  sobelOperator.CreateDirectional();
// Software Guide : EndCodeSnippet

// Software Guide : BeginLatex
//
// The neighborhood iterator is initialized as before, except that now it takes
// its radius directly from the radius of the Sobel operator.  The inner
// product function object is templated over image type and requires no
// initialization.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
  NeighborhoodIteratorType::RadiusType radius = sobelOperator.GetRadius();
  NeighborhoodIteratorType             it(radius, reader->GetOutput(),
                                          reader->GetOutput()->
                                          GetRequestedRegion());

  itk::NeighborhoodInnerProduct<ImageType> innerProduct;
// Software Guide : EndCodeSnippet

// Software Guide : BeginLatex
//
// Using the Sobel operator, inner product, and neighborhood iterator objects,
// we can now write a very simple \code{for} loop for performing convolution
// filtering.  As before, out-of-bounds pixel values are supplied automatically
// by the iterator.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
  for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
    {
    out.Set(innerProduct(it, sobelOperator));
    }
// Software Guide : EndCodeSnippet

// Software Guide : BeginLatex
//
// The output is rescaled and written as in the previous example.  Applying
// this example in the $x$ and $y$ directions produces the images at the center
// and right of Figure~\ref{fig:NeighborhoodExamples1}. Note that x-direction
// operator produces the same output image as in the previous example.
//
// Software Guide : EndLatex

  typedef unsigned char                        WritePixelType;
  typedef otb::Image<WritePixelType, 2>        WriteImageType;
  typedef otb::ImageFileWriter<WriteImageType> WriterType;

  typedef itk::RescaleIntensityImageFilter<
      ImageType, WriteImageType> RescaleFilterType;

  RescaleFilterType::Pointer rescaler = RescaleFilterType::New();

  rescaler->SetOutputMinimum(0);
  rescaler->SetOutputMaximum(255);
  rescaler->SetInput(output);

  WriterType::Pointer writer = WriterType::New();
  writer->SetFileName(argv[2]);
  writer->SetInput(rescaler->GetOutput());
  try
    {
    writer->Update();
    }
  catch (itk::ExceptionObject& err)
    {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return -1;
    }

  return EXIT_SUCCESS;
}
